EMFSA:基于表情符号的多功能融合情绪分析
Hongmei Tang1,2, Wenzhong Tang1, Dixiongxiao Zhu1
1School of Computer Science and Engineering, Beihang University, Beijing, China.
PloS one
|September 19, 2024
概括
本研究引入了基于表情符号的多功能融合情绪分析模型 (EMFSA),以改进短文本分析. EMFSA模型通过整合表情符号,主题和文本功能来增强情绪表达和情感准确性.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 社交媒体上的短文往往缺乏情感深度,而具有丰富的功能.
- 挑战包括语义模两可和稀疏的数据,妨碍准确的情绪分析.
研究的目的:
- 为了提高短文的情感分析准确度.
- 提出一个有效的模型,从短文中提取情感语义.
主要方法:
- 开发了一个基于emoji的多功能融合情绪分析模型 (EMFSA).
- 采用一种预训练方法来提取特征,以及一种情感和表情符号掩盖的语言模型.
- 利用交叉注意力机制进行多功能融合,整合表情符号,主题和文本功能.
主要成果:
- 根据EMFSA模型,三个公共数据集的准确性得到了显著改善.
- 与基线方法相比,获得了2.3%,10.9%和2.7%的准确度增长.
- 有效地增强了情感的语义表达和情感表示准确度.
结论:
- 建议的EMFSA模型有效地解决了短文情绪分析的局限性.
- 多功能融合,特别是结合表情符号语义,对于提高准确性至关重要.
- 该模型显示了社交媒体分析中现实应用的巨大潜力.
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